pandas_apply_operations_to_groups.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Apply Operations To Groups In Pandas\n",
      "\n",
      "- **Author:** [Chris Albon](http://www.chrisalbon.com/), [@ChrisAlbon](https://twitter.com/chrisalbon)\n",
      "- **Date:** -\n",
      "- **Repo:** [Python 3 code snippets for data science](https://github.com/chrisalbon/code_py)\n",
      "- **Note:**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### import modules"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Create dataframe"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], \n",
      "        'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'], \n",
      "        'name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'], \n",
      "        'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],\n",
      "        'postTestScore': [25, 94, 57, 62, 70, 25, 94, 57, 62, 70, 62, 70]}\n",
      "df = pd.DataFrame(raw_data, columns = ['regiment', 'company', 'name', 'preTestScore', 'postTestScore'])\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>regiment</th>\n",
        "      <th>company</th>\n",
        "      <th>name</th>\n",
        "      <th>preTestScore</th>\n",
        "      <th>postTestScore</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> Nighthawks</td>\n",
        "      <td> 1st</td>\n",
        "      <td>   Miller</td>\n",
        "      <td>  4</td>\n",
        "      <td> 25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> Nighthawks</td>\n",
        "      <td> 1st</td>\n",
        "      <td> Jacobson</td>\n",
        "      <td> 24</td>\n",
        "      <td> 94</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> Nighthawks</td>\n",
        "      <td> 2nd</td>\n",
        "      <td>      Ali</td>\n",
        "      <td> 31</td>\n",
        "      <td> 57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> Nighthawks</td>\n",
        "      <td> 2nd</td>\n",
        "      <td>   Milner</td>\n",
        "      <td>  2</td>\n",
        "      <td> 62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>   Dragoons</td>\n",
        "      <td> 1st</td>\n",
        "      <td>    Cooze</td>\n",
        "      <td>  3</td>\n",
        "      <td> 70</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>   Dragoons</td>\n",
        "      <td> 1st</td>\n",
        "      <td>    Jacon</td>\n",
        "      <td>  4</td>\n",
        "      <td> 25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>   Dragoons</td>\n",
        "      <td> 2nd</td>\n",
        "      <td>   Ryaner</td>\n",
        "      <td> 24</td>\n",
        "      <td> 94</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>   Dragoons</td>\n",
        "      <td> 2nd</td>\n",
        "      <td>     Sone</td>\n",
        "      <td> 31</td>\n",
        "      <td> 57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>     Scouts</td>\n",
        "      <td> 1st</td>\n",
        "      <td>    Sloan</td>\n",
        "      <td>  2</td>\n",
        "      <td> 62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>     Scouts</td>\n",
        "      <td> 1st</td>\n",
        "      <td>    Piger</td>\n",
        "      <td>  3</td>\n",
        "      <td> 70</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>     Scouts</td>\n",
        "      <td> 2nd</td>\n",
        "      <td>    Riani</td>\n",
        "      <td>  2</td>\n",
        "      <td> 62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>     Scouts</td>\n",
        "      <td> 2nd</td>\n",
        "      <td>      Ali</td>\n",
        "      <td>  3</td>\n",
        "      <td> 70</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>12 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "      regiment company      name  preTestScore  postTestScore\n",
        "0   Nighthawks     1st    Miller             4             25\n",
        "1   Nighthawks     1st  Jacobson            24             94\n",
        "2   Nighthawks     2nd       Ali            31             57\n",
        "3   Nighthawks     2nd    Milner             2             62\n",
        "4     Dragoons     1st     Cooze             3             70\n",
        "5     Dragoons     1st     Jacon             4             25\n",
        "6     Dragoons     2nd    Ryaner            24             94\n",
        "7     Dragoons     2nd      Sone            31             57\n",
        "8       Scouts     1st     Sloan             2             62\n",
        "9       Scouts     1st     Piger             3             70\n",
        "10      Scouts     2nd     Riani             2             62\n",
        "11      Scouts     2nd       Ali             3             70\n",
        "\n",
        "[12 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Create a groupby variable that groups preTestScores by regiment"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "groupby_regiment = df['preTestScore'].groupby(df['company'])\n",
      "groupby_regiment"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "<pandas.core.groupby.SeriesGroupBy object at 0x1075fb610>"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\"This grouped variable is now a GroupBy object. It has not actually computed anything yet except for some intermediate data about the group key df['key1']. The idea is that this object has all of the information needed to then apply some operation to each of the groups.\" - Python for Data Analysis"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### View a grouping\n",
      "\n",
      "Use list() to show what a grouping looks like"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "list(df['preTestScore'].groupby(df['company']))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "[('1st', 0     4\n",
        "  1    24\n",
        "  4     3\n",
        "  5     4\n",
        "  8     2\n",
        "  9     3\n",
        "  Name: preTestScore, dtype: int64), ('2nd', 2     31\n",
        "  3      2\n",
        "  6     24\n",
        "  7     31\n",
        "  10     2\n",
        "  11     3\n",
        "  Name: preTestScore, dtype: int64)]"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Descriptive statistics by group"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['preTestScore'].groupby(df['company']).describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "company       \n",
        "1st      count     6.000000\n",
        "         mean      6.666667\n",
        "         std       8.524475\n",
        "         min       2.000000\n",
        "         25%       3.000000\n",
        "         50%       3.500000\n",
        "         75%       4.000000\n",
        "         max      24.000000\n",
        "2nd      count     6.000000\n",
        "         mean     15.500000\n",
        "         std      14.652645\n",
        "         min       2.000000\n",
        "         25%       2.250000\n",
        "         50%      13.500000\n",
        "         75%      29.250000\n",
        "         max      31.000000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Mean of each regiment's preTestScore"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "groupby_regiment.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "company\n",
        "1st         6.666667\n",
        "2nd        15.500000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Mean preTestScores grouped by regiment and company"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['preTestScore'].groupby([df['regiment'], df['company']]).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "regiment    company\n",
        "Dragoons    1st         3.5\n",
        "            2nd        27.5\n",
        "Nighthawks  1st        14.0\n",
        "            2nd        16.5\n",
        "Scouts      1st         2.5\n",
        "            2nd         2.5\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Mean preTestScores grouped by regiment and company without heirarchical indexing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['preTestScore'].groupby([df['regiment'], df['company']]).mean().unstack()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>company</th>\n",
        "      <th>1st</th>\n",
        "      <th>2nd</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>regiment</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Dragoons</th>\n",
        "      <td>  3.5</td>\n",
        "      <td> 27.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Nighthawks</th>\n",
        "      <td> 14.0</td>\n",
        "      <td> 16.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Scouts</th>\n",
        "      <td>  2.5</td>\n",
        "      <td>  2.5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "company      1st   2nd\n",
        "regiment              \n",
        "Dragoons     3.5  27.5\n",
        "Nighthawks  14.0  16.5\n",
        "Scouts       2.5   2.5\n",
        "\n",
        "[3 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Group the entire dataframe by regiment and company"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.groupby(['regiment', 'company']).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>preTestScore</th>\n",
        "      <th>postTestScore</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>regiment</th>\n",
        "      <th>company</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Dragoons</th>\n",
        "      <th>1st</th>\n",
        "      <td>  3.5</td>\n",
        "      <td> 47.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2nd</th>\n",
        "      <td> 27.5</td>\n",
        "      <td> 75.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Nighthawks</th>\n",
        "      <th>1st</th>\n",
        "      <td> 14.0</td>\n",
        "      <td> 59.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2nd</th>\n",
        "      <td> 16.5</td>\n",
        "      <td> 59.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Scouts</th>\n",
        "      <th>1st</th>\n",
        "      <td>  2.5</td>\n",
        "      <td> 66.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2nd</th>\n",
        "      <td>  2.5</td>\n",
        "      <td> 66.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>6 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "                    preTestScore  postTestScore\n",
        "regiment   company                             \n",
        "Dragoons   1st               3.5           47.5\n",
        "           2nd              27.5           75.5\n",
        "Nighthawks 1st              14.0           59.5\n",
        "           2nd              16.5           59.5\n",
        "Scouts     1st               2.5           66.0\n",
        "           2nd               2.5           66.0\n",
        "\n",
        "[6 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Number of observations in each regiment and company"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.groupby(['regiment', 'company']).size()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "regiment    company\n",
        "Dragoons    1st        2\n",
        "            2nd        2\n",
        "Nighthawks  1st        2\n",
        "            2nd        2\n",
        "Scouts      1st        2\n",
        "            2nd        2\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Iterate an operations over groups"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Group the dataframe by regiment, and for each regiment,\n",
      "for name, group in df.groupby('regiment'): \n",
      "    # print the name of the regiment\n",
      "    print(name)\n",
      "    # print the data of that regiment\n",
      "    print(group)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Dragoons\n",
        "   regiment company    name  preTestScore  postTestScore\n",
        "4  Dragoons     1st   Cooze             3             70\n",
        "5  Dragoons     1st   Jacon             4             25\n",
        "6  Dragoons     2nd  Ryaner            24             94\n",
        "7  Dragoons     2nd    Sone            31             57\n",
        "\n",
        "[4 rows x 5 columns]\n",
        "Nighthawks\n",
        "     regiment company      name  preTestScore  postTestScore\n",
        "0  Nighthawks     1st    Miller             4             25\n",
        "1  Nighthawks     1st  Jacobson            24             94\n",
        "2  Nighthawks     2nd       Ali            31             57\n",
        "3  Nighthawks     2nd    Milner             2             62\n",
        "\n",
        "[4 rows x 5 columns]\n",
        "Scouts\n",
        "   regiment company   name  preTestScore  postTestScore\n",
        "8    Scouts     1st  Sloan             2             62\n",
        "9    Scouts     1st  Piger             3             70\n",
        "10   Scouts     2nd  Riani             2             62\n",
        "11   Scouts     2nd    Ali             3             70\n",
        "\n",
        "[4 rows x 5 columns]\n"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Group by columns\n",
      "\n",
      "Specifically in this case: group by the data types of the columns (i.e. axis=1) and then use list() to view what that grouping looks like"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "list(df.groupby(df.dtypes, axis=1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": [
        "[(dtype('int64'),     preTestScore  postTestScore\n",
        "  0              4             25\n",
        "  1             24             94\n",
        "  2             31             57\n",
        "  3              2             62\n",
        "  4              3             70\n",
        "  5              4             25\n",
        "  6             24             94\n",
        "  7             31             57\n",
        "  8              2             62\n",
        "  9              3             70\n",
        "  10             2             62\n",
        "  11             3             70\n",
        "  \n",
        "  [12 rows x 2 columns]), (dtype('O'),       regiment company      name\n",
        "  0   Nighthawks     1st    Miller\n",
        "  1   Nighthawks     1st  Jacobson\n",
        "  2   Nighthawks     2nd       Ali\n",
        "  3   Nighthawks     2nd    Milner\n",
        "  4     Dragoons     1st     Cooze\n",
        "  5     Dragoons     1st     Jacon\n",
        "  6     Dragoons     2nd    Ryaner\n",
        "  7     Dragoons     2nd      Sone\n",
        "  8       Scouts     1st     Sloan\n",
        "  9       Scouts     1st     Piger\n",
        "  10      Scouts     2nd     Riani\n",
        "  11      Scouts     2nd       Ali\n",
        "  \n",
        "  [12 rows x 3 columns])]"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### In the dataframe \"df\", group by \"regiments, take the mean values of the other variables for those groups, then display them with the prefix_mean"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.groupby('regiment').mean().add_prefix('mean_')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mean_preTestScore</th>\n",
        "      <th>mean_postTestScore</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>regiment</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Dragoons</th>\n",
        "      <td> 15.50</td>\n",
        "      <td> 61.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Nighthawks</th>\n",
        "      <td> 15.25</td>\n",
        "      <td> 59.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Scouts</th>\n",
        "      <td>  2.50</td>\n",
        "      <td> 66.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 72,
       "text": [
        "            mean_preTestScore  mean_postTestScore\n",
        "regiment                                         \n",
        "Dragoons                15.50                61.5\n",
        "Nighthawks              15.25                59.5\n",
        "Scouts                   2.50                66.0\n",
        "\n",
        "[3 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Create a function to get the stats of a group"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_stats(group):\n",
      "    return {'min': group.min(), 'max': group.max(), 'count': group.count(), 'mean': group.mean()}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 73
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Create bins and bin up postTestScore by those pins"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bins = [0, 25, 50, 75, 100]\n",
      "group_names = ['Low', 'Okay', 'Good', 'Great']\n",
      "df['categories'] = pd.cut(df['postTestScore'], bins, labels=group_names)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 75
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Apply the get_stats() function to each postTestScore bin"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['postTestScore'].groupby(df['categories']).apply(get_stats).unstack()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>count</th>\n",
        "      <th>max</th>\n",
        "      <th>mean</th>\n",
        "      <th>min</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>categories</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Good</th>\n",
        "      <td> 8</td>\n",
        "      <td> 70</td>\n",
        "      <td> 63.75</td>\n",
        "      <td> 57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Great</th>\n",
        "      <td> 2</td>\n",
        "      <td> 94</td>\n",
        "      <td> 94.00</td>\n",
        "      <td> 94</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Low</th>\n",
        "      <td> 2</td>\n",
        "      <td> 25</td>\n",
        "      <td> 25.00</td>\n",
        "      <td> 25</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "            count  max   mean  min\n",
        "categories                        \n",
        "Good            8   70  63.75   57\n",
        "Great           2   94  94.00   94\n",
        "Low             2   25  25.00   25\n",
        "\n",
        "[3 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 80
    }
   ],
   "metadata": {}
  }
 ]
}